Epidermis recreation in spongy-like hydrogels: New opportunities to explore epidermis-like analogues

[Excerpt] On the road to successfully achieving skin regeneration, 3D matrices/scaffolds that provide the adequate physico-chemical and biological cues to recreate the ideal healing environment are believed to be a key element [1], [2] and [3]. Numerous polymeric matrices derived from both natural [4] and [5] and synthetic [6], [7] and [8] sources have been used as cellular supports; nowadays, fewer matrices are simple carriers, and more and more are ECM analogues that can actively participate in the healing process. Therefore, the attractive characteristics of hydrogels, such as high water content, tunable elasticity and facilitated mass transportation, have made them excellent materials to mimic cells’ native environment [9]. Moreover, their hygroscopic nature [10] and possibility of attaining soft tissues-like mechanical properties mean they have potential for exploitation as wound healing promoters [11], [12], [13] and [14]. Nonetheless, hydrogels lack natural cell adhesion sites [15], which limits the maximization of their potential in the recreation of the cell niche. This issue has been tackled through the use of a range of sophisticated approaches to decorate the hydrogels with adhesion sequences such as arginine-glycine-aspartic acid (RGD) derived from fibronectin [16], [17] and [18], and tyrosine-isoleucine-glycine-serine-arginine (YIGSR) derived from laminin [18] and [19], which not only aim to modulate cell adhesion, but also influencing cell fate and survival [18]. Nonetheless, its widespread use is still limited by significant costs associated with the use of recombinant bioactive molecules.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Expressão e caracterização de uma lectina recombinante de interesse biomédico

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Engenharia Clínica)